Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107084
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dc.contributor.authorAnamitra Makur (EEE)en
dc.contributor.authorSahoo, Sujit Kumaren
dc.date.accessioned2015-03-30T08:51:54Zen
dc.date.accessioned2019-12-06T22:24:23Z-
dc.date.available2015-03-30T08:51:54Zen
dc.date.available2019-12-06T22:24:23Z-
dc.date.copyright2015en
dc.date.issued2015en
dc.identifier.citationSujit, K. S., & Anamitra Makur. (2015). Enhancing image denoising by controlling noise incursion in learned dictionaries. IEEE signal processing letters, 22(8), 1123-1126.en
dc.identifier.urihttps://hdl.handle.net/10356/107084-
dc.description.abstractExisting image denoising frameworks via sparse representation using learned dictionaries have an weakness that the dictionary, trained from noisy image, suffers from noise incursion. This paper analyzes this noise incursion, explicitly derives the noise component in the dictionary update step, and provides a simple remedy for a desired signal to noise ratio. The remedy is shown to perform better both in objective and subjective measures for lesser computation, and complements the framework of image denoising.en
dc.format.extent4 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE signal processing lettersen
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2015.2388712].en
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen
dc.titleEnhancing image denoising by controlling noise incursion in learned dictionariesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1109/LSP.2015.2388712en
dc.description.versionAccepted versionen
dc.identifier.rims183149en
item.grantfulltextopen-
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